CN114779846B - Intelligent electric heating control system and method for large tank - Google Patents
Intelligent electric heating control system and method for large tank Download PDFInfo
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- CN114779846B CN114779846B CN202210608259.9A CN202210608259A CN114779846B CN 114779846 B CN114779846 B CN 114779846B CN 202210608259 A CN202210608259 A CN 202210608259A CN 114779846 B CN114779846 B CN 114779846B
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- 238000005485 electric heating Methods 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 title claims abstract description 10
- 238000010438 heat treatment Methods 0.000 claims abstract description 101
- 238000013528 artificial neural network Methods 0.000 claims abstract description 19
- 239000007788 liquid Substances 0.000 claims abstract description 13
- 239000007787 solid Substances 0.000 claims abstract description 13
- 210000002364 input neuron Anatomy 0.000 claims abstract description 6
- 238000012544 monitoring process Methods 0.000 claims abstract description 3
- 210000004205 output neuron Anatomy 0.000 claims abstract description 3
- 210000002569 neuron Anatomy 0.000 claims description 18
- 238000001514 detection method Methods 0.000 claims description 17
- 238000011478 gradient descent method Methods 0.000 claims description 8
- 101001128814 Pandinus imperator Pandinin-1 Proteins 0.000 claims description 7
- 101001024685 Pandinus imperator Pandinin-2 Proteins 0.000 claims description 6
- 239000012528 membrane Substances 0.000 claims description 6
- 239000003990 capacitor Substances 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 239000010409 thin film Substances 0.000 claims description 2
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- 230000001105 regulatory effect Effects 0.000 abstract description 2
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- 101100244014 Neurospora crassa (strain ATCC 24698 / 74-OR23-1A / CBS 708.71 / DSM 1257 / FGSC 987) ppi-5 gene Proteins 0.000 description 1
- 101150023294 PIN4 gene Proteins 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B1/00—Details of electric heating devices
- H05B1/02—Automatic switching arrangements specially adapted to apparatus ; Control of heating devices
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention discloses an intelligent electric heating control system and method for a large tank, wherein the system comprises a main controller, a secondary controller, a temperature monitoring module, a parameter setting module, a control algorithm module and a Solid State Relay (SSR) output module. The control algorithm adopts a self-adaptive neural network, takes the volume of the tank, the specific heat capacity of liquid in the tank, the set heating temperature, the heating time and the real-time temperature in the tank as input neurons and the number of heating channels and the heating power as output neurons. The key of the system is that the volume of the tank, the specific heat capacity of liquid in the tank, the set temperature, the heating time, the real-time temperature, the number of heating channels and the heating power are connected, the real-time temperature is input into the neural network, and the heating temperature is adjusted and controlled. Through this heating control system, can the automatically regulated not heating parameter of unidimensional tank case, whole control system operation is stable, and control error is little, has realized the intelligent heating and the temperature control to the interior liquid of large-scale tank case.
Description
Technical Field
The invention belongs to the technical field of tank control, and particularly relates to an intelligent electric heating control system and method for a large tank.
Background
With the development of the remote storage and transportation industry, the tank container has been rapidly developed in recent years, the application of the tank container is more and more widespread, and the electrically heated tank container is increasingly applied in the chemical industry. At present, an electric heating system for the tank container mainly depends on import, and related researches and products are still lacking in China. Therefore, the intelligent electric heating system suitable for the large tank can be researched to effectively solve the current urgent need of the electric heating tank.
Disclosure of Invention
The invention aims to: aiming at the prior art, an intelligent electric heating control system and method for a large tank are provided.
The technical scheme is as follows: an intelligent electric heating control system for a large tank comprises a primary controller, a secondary controller, a solid-state relay output module, a heating module and a temperature detection module which are sequentially connected in series; the temperature detection module is also connected with the secondary controller;
the primary controller comprises a main controller, a display module, a control algorithm module and a parameter setting module; the display module, the control algorithm module and the parameter setting module are all connected to the main controller, the parameter setting module comprises a membrane switch key, and the membrane switch key is connected to the control algorithm module through a circuit;
the secondary controller comprises a secondary controller 1,2, & 10; the plurality of auxiliary controllers are connected to the main controller;
the solid state relay output module comprises solid state relays 1,2, 10, wherein each solid state relay is connected with an SSR driving circuit, and the solid state relays and the auxiliary controllers are connected in a one-to-one correspondence manner according to numbers;
the heating module comprises resistance heaters 1,2, 10, wherein the resistance heaters and the solid state relays are connected in a one-to-one correspondence manner according to numbers, and 10 resistance heaters are distributed at different positions of the tank to heat a plurality of positions of the tank;
the temperature detection module comprises temperature sensors 1,2, & gt, 10, wherein each temperature sensor is connected to a temperature acquisition circuit, the temperature sensors are connected with the resistance heater and the auxiliary controller in a one-to-one correspondence manner according to numbers, and are used for acquiring temperatures at different positions of the tank and sending acquisition signals to the auxiliary controller so as to control a heating state;
the control algorithm module comprises a self-adaptive neural network, tank information is used as an input neuron, heating information is used as an output neuron, heating information is input into the main controller, and hierarchical control is carried out through the main controller and the auxiliary controller, so that heating control of the tank is realized.
Preferably, the tank information comprises tank volume, specific heat capacity of liquid in the tank, set heating temperature, heating time and real-time temperature in the tank, and the heating information comprises heating channel number and heating power.
Preferably, the adaptive neural network comprises a neural network comprising 5 neurons X 1 、X 2 、X 3 、X 4 、X 5 Is an input layer of 7 neurons, an hidden layer of 2 neurons Y 1 、Y 2 An output layer of (2); wherein neuron X 1 、X 2 、X 3 、X 4 、X 5 Respectively representing the volume of the tank, the specific heat capacity of liquid in the tank, the set heating temperature, the heating time and the real-time temperature in the tank, and the neuron Y 1 、Y 2 The thin film switch key is connected with the neuron X through a circuit 1 、X 2 、X 3 、X 4 、X 5 。
Preferably, the heating temperature X will be set 3 Real-time temperature X in tank 5 The square of the difference is taken as a setting target A:
number of heating channels and heating power Y 1 、Y 2 Two parameters are self-set by adopting a gradient descent method:
D(t)＝ΔD+αD(t-1)
wherein: alpha is an inertia factor, and takes on values (0, 1); d (t) is a weight gradient at the moment of t, and a gradient descent method is adopted to enable the weight adjustment quantity to be in direct proportion to the negative gradient of A (t), so that:
wherein:as Jacobian information of the controlled object, eta is a learning factor and takes on the value (0, 1), thereby being capable of calculating Y 1 、Y 2 。
Preferably, the SSR driving circuit includes a diode D2, the positive electrode of the diode D2 is connected to a 3.3V dc positive electrode, the negative electrode is connected to a resistor R1, the gate of the power tube Q2 is connected to the co-controller and R1, the drain is connected to a resistor R4 and an optocoupler Pin1, the source is connected to GND and optocoupler Pin2, the capacitor C1 is connected to the source, the gate and optocouplers Pin1 and Pin2 of the power tube, the ssr+ is connected to optocoupler Pin3, the SSR-is connected to optocoupler Pin4, one end of the capacitor C2 is connected to optocoupler Pin3 and ssr+, the other end is connected to optocoupler Pin4 and SSR-, the positive electrode of the diode D2 is connected to optocoupler Pin4 and SSR-, and the negative electrode is connected to optocoupler Pin3 and ssr+.
Preferably, the temperature acquisition circuit comprises a 24-bit-precision CS1237 analog-to-digital conversion chip, resistors R6, R10 and R9 are connected to Pin3 in series, resistors R7 and R11 are connected to Pin4 in series, and Pin5 and Pin6 are connected to the main controller.
An intelligent electric heating control method for a large tank comprises the following steps: after starting to work, firstly initializing a program, reading input neuron parameters of the self-adaptive neural network from a parameter input module by a main controller, obtaining the number of heating channels and heating power after calculation of the self-adaptive neural network, respectively heating different areas of the tank box with different powers, realizing the consistency of the overall temperature of the tank box, monitoring temperature information in real time by a temperature detection module, comparing the real-time temperature with a set temperature, continuing heating when the temperature does not reach the set temperature value, changing the heating power along with the change of the difference value between the actual temperature and the set temperature, and ending heating when the actual temperature reaches the set temperature.
The beneficial effects are that: the key of the invention is that the volume of the tank, the specific heat capacity of liquid in the tank, the set temperature, the heating time, the real-time temperature, the number of heating channels and the heating power are connected, and the real-time temperature is input into the neural network to realize the real-time adjustment of the heating channels and the heating power, thereby realizing the adjustment and control of the heating temperature. Through this heating control system, can the automatically regulated not heating parameter of unidimensional tank case, whole control system operation is stable, and control error is little, has realized the intelligent heating and the temperature control to the interior liquid of large-scale tank case.
Drawings
FIG. 1 is a system overall structure of the present invention;
FIG. 2 is a neural network algorithm for tank heating control of the present invention;
FIG. 3 is a flow chart of a control method of the present invention;
FIG. 4 is a circuit diagram of an input module of the present invention;
FIG. 5 is a circuit diagram of an SSR drive circuit of the present invention;
FIG. 6 is a temperature acquisition circuit of the present invention;
fig. 7 is a single channel electrical diagram of the tank of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings.
As shown in fig. 1, the overall control system of the overall system structure comprises: the device comprises a first-stage controller, a second-stage controller, a solid-state relay output module, a heating module and a temperature detection module. The primary controller comprises a main controller, a display module, a control algorithm module and a parameter setting module; the secondary controller consists of 10 auxiliary controllers; the solid state relay output module comprises 10 solid state relays and controls the working state of the heating module; the heating module comprises 10 resistance heaters which are distributed at different positions of the tank and heat a plurality of positions of the tank; the temperature detection module is composed of 10 temperature sensors, collects temperatures at different positions of the tank and sends collected signals to the auxiliary controller, and therefore heating states are controlled. In the primary controller, the parameter setting module inputs the parameters of tank volume, liquid specific heat capacity in the tank, set heating temperature and heating time into the main controller, the main controller processes and calculates data by using the control algorithm module, and the display module can display the temperatures of different positions of the tank and the heating power of each path acquired by the current temperature detection module. The main controller is communicated with the auxiliary controllers to realize the circulation of data and commands, the main controller converts the result after data processing into a signal to send an instruction to the auxiliary controllers, and each auxiliary controller is used for specifically controlling the heating on-off and the heating power of one path and feeding back the temperature signal received from the temperature detection module to the main controller.
As shown in fig. 2. The patent adopts a neural network structure with a 5-7-2 structure, an input layer is provided with 5 neurons, X 1 、X 2 、X 3 、X 4 、X 5 Respectively representing the volume of the tank, the specific heat capacity of liquid in the tank, the set heating temperature, the heating time and the real-time temperature in the tank. The output layer has two neurons, Y 1 、Y 2 The representations represent the number of heating channels and the heating power, respectively, and the hidden layer selects 7 neurons. The neural network is trained through experimental samples, and the final realization effect is that the parameters of tank volume, specific heat capacity of liquid in the tank, set heating temperature and heating time are input into a parameter input module, then the current real-time temperature is obtained from a temperature detection module, and the real-time heating channel number and heating power can be obtained after the calculation of the neural network through the 5 parameter values.
As shown in fig. 3, after the system starts to work, the whole control flow is initialized by the program, then the main controller reads the input neuron parameters of the neural network controller from the parameter input module, the heating channel number and the heating power are obtained after the calculation of the neural network, and the heating channels and the heating power are used for heating different areas of the tank respectively, so that the consistency of the whole temperature of the tank is realized. The temperature detection module monitors temperature information in real time, compares the real-time temperature with the set temperature, and when the temperature does not reach the set temperature value, heating is continued, heating power changes along with the change of the difference value between the actual temperature and the set temperature, and when the actual temperature reaches the set temperature, heating is finished.
In the neural network structure selection, the input layer has 5 neurons, the hidden layer has 7 neurons, and the output layer has 2 neurons. Taking the square of the difference between the set temperature X3 and the actual temperature X5 as a setting target A, namely:
Y 1 、Y 2 two parameters are self-set by adopting a gradient descent method:
D(t)＝ΔD+αD(t-1)
wherein: alpha is an inertia factor, and takes on values (0, 1); d (t) is a weight gradient at time t. The gradient descent method is adopted to make the weight adjustment quantity proportional to the negative gradient of A (t), then:
wherein:the Jacobian information of the controlled object is that eta is a learning factor and takes a value of (0, 1). From this, Y can be calculated 1 、Y 2 。
As shown in fig. 4. In order to facilitate an operator to set the heating temperature of the tank, the principle diagram of the input module is that the tank volume, the specific heat capacity of liquid in the tank, the heating temperature and the heating time are set by adopting a method of a film switch key, and the tank is heated by an optical coupler U 8 Isolation is carried out, and interference of the outside to the ARM controller is avoided. The Pkey_up channel is externally connected with a membrane switch, and if a key is pressed down, the Pkey_up is at high potential and passes through a resistor R 31 Opening the optocoupler U 8 Pin5, pin6 of the optical coupler U 8 The output sides Pin11 and Pin12 are conducted, VCC3.3V are scanned by R28, pin12 and Pin11 to GND, the ARM end scans the key_UP Pin to be low potential, and the key input signal is responded. The working flow of other pins is the same as the working flow, and parameter setting is realized through keys of the membrane switch.
SSR drive circuit as shown in FIG. 5, the circuit uses Q 2 Amplifying the power of ARM output signals to drive the optical coupler U 2 To the SSR drive end. Wherein D is 2 And R is 1 To indicate the state of the driving circuit, PWM_OUT is high, D 2 Shut down, Q 2 Opening U 2 The low potential is between Pin1 and Pin2, the optocoupler is closed, and SSR output is closed; PWM_OUT is low, D 2 Open, Q 2 Closing, optocoupler U 2 Current flows between Pin1 and Pin2, the optocoupler is turned on, U 2 Pin3 and Pin4 are communicated, the SSR+ and SSR-are communicated, the external SSR is communicated, and the tank is heated.
As shown in fig. 6. And the temperature acquisition circuit uses a 24-bit-precision CS1237 analog-to-digital conversion chip to improve the detection precision of the system. Pin1 of the data acquisition end is used as a reference source input, pin8 is used as a reference source output, and an output reference voltage value of the temperature sensor is set; pin3 is AINN channel negative input, pin4 is AINP is channel positive input, voltage values of a temperature sensor are detected through positive and negative ends input of signal acquisition, resistance values of the sensor change along with different temperatures in a tank, voltage values obtained by a detection end change along with the changes, and the change values are acquired through the detection end; the detected output adopts SPI communication protocol, and the detected data is transmitted to the ARM processor through communication between PT100_Dout (data) and PT100_Sclk (clock). The chip converts analog quantity into digital quantity, calculates the change proportion and obtains the detected analog quantity temperature value.
As shown in fig. 7. The single-channel electric diagram of the tank consists of a fuse, an air switch with leakage protection, a solid-state relay and an electric heating wire. The three-phase 380V of the power grid enters the control system, the power supply control of the system is realized through an air switch with leakage protection, a solid-state relay is protected through a fuse, the electric heating part of the tank is controlled by the switch of the solid-state relay, and an electric heating wire is arranged on the outer side wall of the tank. After receiving the working signal of the auxiliary controller, the solid-state relay is closed, the resistance heater begins to work to heat the tank, and along with the change of temperature, the heating power also changes, so that the heating rate is ensured, and overshoot is prevented from occurring at the same time. If the power circuit fails, on one hand, the leakage protection switch acts and the output is closed; on the other hand, the fuse blows, disconnecting the device.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (5)
1. The intelligent electric heating control system for the large tank is characterized by comprising a primary controller, a secondary controller, a solid-state relay output module, a heating module and a temperature detection module which are sequentially connected in series; the temperature detection module is also connected with the secondary controller;
the primary controller comprises a main controller, a display module, a control algorithm module and a parameter setting module;
the display module, the control algorithm module and the parameter setting module are all connected to the main controller, the parameter setting module comprises a membrane switch key, and the membrane switch key is connected to the control algorithm module through a circuit;
the secondary controller comprises a secondary controller 1,2, & 10; the plurality of auxiliary controllers are connected to the main controller;
the solid state relay output module comprises solid state relays 1,2, 10, wherein each solid state relay is connected with an SSR driving circuit, and the solid state relays and the auxiliary controllers are connected in a one-to-one correspondence manner according to numbers;
the heating module comprises resistance heaters 1,2, 10, wherein the resistance heaters and the solid state relays are connected in a one-to-one correspondence manner according to numbers, and 10 resistance heaters are distributed at different positions of the tank to heat a plurality of positions of the tank;
the temperature detection module comprises temperature sensors 1,2, & gt, 10, wherein each temperature sensor is connected to a temperature acquisition circuit, the temperature sensors are connected with the resistance heater and the auxiliary controller in a one-to-one correspondence manner according to numbers, and are used for acquiring temperatures at different positions of the tank and sending acquisition signals to the auxiliary controller so as to control a heating state;
the control algorithm module is a self-adaptive neural network, takes tank information as an input neuron and heating information as an output neuron, inputs the heating information into the main controller, and performs hierarchical control with the auxiliary controller through the main controller to realize heating control of the tank;
the SSR driving circuit comprises a diode D2, wherein the positive electrode of the diode D2 is connected with a 3.3V direct current positive electrode, the negative electrode of the diode D2 is connected with a resistor R1, the grid electrode of a power tube Q2 is connected with a secondary controller and a resistor R1, the drain electrode of the power tube Q2 is connected with an optocoupler Pin1, the source electrode of the power tube Q2 is connected with GND and the optocoupler Pin2, the capacitor C1 is connected with the source electrode, the drain electrode and the optocouplers Pin1 and Pin2 of the power tube Q2, SSR+ is connected with the optocoupler Pin3, SSR-is connected with the optocoupler Pin4, one end of the capacitor C2 is connected with the optocoupler Pin3 and SSR+, the other end of the capacitor C2 is connected with the optocoupler Pin4 and SSR-, and the negative electrode is connected with the optocoupler Pin3 and SSR+;
heating temperature x at sampling time t 3 (t) real-time temperature x in tank with sampling time t 5 (t) square of the difference as a setting target a (t) for the sampling time t is:
number of heating channels Y 1 And heating power Y 2 Two parameters are self-set by adopting a gradient descent method:
D(t)＝ΔD+αD(t-1)
wherein: alpha is an inertia factor, and takes on values (0, 1); d (t) is a weight gradient at the moment of t, and a gradient descent method is adopted to enable the weight adjustment quantity to be in direct proportion to the negative gradient of A (t), so that:
wherein:for Jacobian Jacobian information of the controlled object, η is a learning factor, and is a value (0, 1), thereby calculating Y 1 、Y 2 。
2. The intelligent electrical heating control system of claim 1, wherein the tank information comprises tank volume, specific heat capacity of liquid in the tank, set heating temperature, heating time and real-time temperature in the tank, and the heating information comprises heating channel number and heating power.
3. A large tank intelligent electrical heating control system as claimed in claim 2, wherein said adaptive neural network comprises a system comprising 5 neurons X 1 、X 2 、X 3 、X 4 、X 5 Is an input layer of 7 neurons, an hidden layer of 2 neurons Y 1 、Y 2 An output layer of (2); wherein neuron X 1 、X 2 、X 3 、X 4 、X 5 Respectively representing the volume of the tank, the specific heat capacity of liquid in the tank, the set heating temperature, the heating time and the real-time temperature in the tank, and the neuron Y 1 、Y 2 The thin film switch key is connected with the neuron X through a circuit 1 、X 2 、X 3 、X 4 、X 5 。
4. A large tank intelligent electrical heating control system as claimed in claim 3, wherein the temperature acquisition circuit comprises a 24-bit precision CS1237 analog-to-digital conversion chip, resistors R6, R10, R9 are connected in series to Pin3, resistors R7, R11 are connected in series to Pin4, and pins 5, pin6 are connected to the main controller.
5. A large tank intelligent electric heating control method applied to the large tank intelligent electric heating control system as set forth in any one of claims 1 to 4, characterized by comprising the following steps: after starting to work, firstly initializing a program, reading input neuron parameters of the self-adaptive neural network from a parameter input module by a main controller, obtaining the number of heating channels and heating power after calculation of the self-adaptive neural network, respectively heating different areas of the tank box with different powers to realize consistency of the overall temperature of the tank box, monitoring temperature information in real time by a temperature detection module, comparing the real-time temperature with a set temperature, continuing heating when the temperature does not reach the set temperature value, changing the heating power along with the change of the difference between the actual temperature and the set temperature, and ending heating when the actual temperature reaches the set temperature;
heating temperature x at sampling time t 3 (t) real-time temperature x in tank with sampling time t 5 (t) square of the difference as a setting target a (t) for the sampling time t is:
number of heating channels Y 1 And heating power Y 2 Two parameters are self-set by adopting a gradient descent method:
D(t)＝ΔD+αD(t-1)
wherein: alpha is an inertia factor, and takes on values (0, 1); d (t) is a weight gradient at the moment of t, and a gradient descent method is adopted to enable the weight adjustment quantity to be in direct proportion to the negative gradient of A (t), so that:
wherein:for Jacobian Jacobian information of the controlled object, η is a learning factor, and is a value (0, 1), thereby calculating Y 1 、Y 2 。
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| CN112230692A (en) * | 2020-10-27 | 2021-01-15 | 浙江大学 | Multi-path temperature control system |
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| US11919124B2 (en) * | 2018-12-28 | 2024-03-05 | Ebara Corporation | Pad-temperature regulating apparatus, method of regulating pad-temperature, polishing apparatus, and polishing system |
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|---|---|---|---|---|
| CN104275785A (en) * | 2014-09-23 | 2015-01-14 | 格力电器（武汉）有限公司 | Temperature control device and hot runner mold |
| CN105589382A (en) * | 2015-12-22 | 2016-05-18 | 江阴市辉龙电热电器有限公司 | Heater alarm and control module |
| CN105807811A (en) * | 2016-03-14 | 2016-07-27 | 东华大学 | Remote greenhouse temperature control system based on WI-FI |
| CN106168815A (en) * | 2016-07-27 | 2016-11-30 | 中冶南方工程技术有限公司 | A kind of acid liquor temperature control system based on Neural network PID and method |
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| CN112230692A (en) * | 2020-10-27 | 2021-01-15 | 浙江大学 | Multi-path temperature control system |
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